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Resource Allocation in Cloud Computing. Resource Allocation. How to maximize resources in order to most effectively provide cloud computing services. Presentation Plan. Distributed Clouds and their Challenges IaaS Resource Allocation based on pre-known demands

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Resource allocation in cloud computing

Resource Allocation


Cloud Computing

Resource allocation
Resource Allocation

  • How to maximize resources in order to most effectively provide cloud computing services

Resource Allocation in Cloud Computing

Presentation plan
Presentation Plan

  • Distributed Clouds and their Challenges

  • IaaS

  • Resource Allocation based on pre-known demands

  • On the fly Resource Allocation

  • SLA

  • Resource Pricing and Equilibrium Allocation Policy

  • Questions

Resource Allocation in Cloud Computing

Papers used
Papers Used

  • Debraj:2. D. Ardagna, M. Trubian, and L. Zhang, “SLA based resource allocation policies in autonomic environments,” Journal of Parallel and Distributed Computing, vol. 67, 2007, pp. 259-270.9.Domain based resource management by Dongwan Shin and HakanAkkan (Secure Computing Laboratory ,Department of Computer Science and Engineering, New Mexico Tech, Socorro, NM 87801 Kiran:1. W. Daniel, “Exploiting dynamic resource allocation for efficient parallel data processing in the cloud,” IEEE Transactions on Parallel and Distributed Systems, vol. 22, 2011, pp. 985-997.3. S. Di and C.-L. Wang, “Dynamic optimization of multiattribute resource allocation in self organizing clouds,” IEEE Transactions on Parallel and Distributed Systems, vol. 24, 2013,Patricia:4. P. T. Endo, A. V. de Almeida Palhares, N. N. Pereira, G. E. Goncalves, D. Sadok, J. Kelner, B. Melander, and J. E. Mangs, "Resource allocation for distributed cloud: concepts and research challenges," Network, IEEE, vol. 25, pp. 42-46.6. T. Fei, Magoule, x, and F. s, “Resource pricing and equilibrium allocation policy in cloud computing,” Computer and Information Technology, 2010, pp. 195-202.8. T. Dillon, C. Wu, and E. Chang, “Cloud Computing: Issues and Challenges,” IEEE Int'l. Conf. Advanced Info. Networking and Apps., 2010, pp. 27-33.Prasanna:5. G. Wei, A. Vasilakos, Y. Zheng, and N. Xiong, “A game-theoretic method of fair resource allocation for cloud computing services,” Supercomputing, vol. 54, 2010, pp. 252-269.7. Yazir, Y.O.; Matthews, C.; Farahbod, R.; Neville, S.; Guitouni, A.; Ganti, S.; Coady, Y.; , "Dynamic Resource Allocation in Computing Clouds Using Distributed Multiple Criteria Decision Analysis," Cloud Computing (CLOUD), 2010 IEEE 3rd International Conference on , vol., no., pp.91-98, 5-10 July 2010doi: 10.1109/CLOUD.2010.66

Resource Allocation in Cloud Computing

Resource allocation in cloud computing

Resource Allocation in Cloud Computing

Distributed Clouds



- Patricia

Current cloud computing providers

Resource Allocation in Cloud Computing

Current Cloud Computing Providers

  • Large, consolidated data centers

  • Problems

    • Resource over-provisioning

    • Heat dissipation/temperature control

    • Greater average distance to users

Distributed clouds benefits

Resource Allocation in Cloud Computing

Distributed Clouds: Benefits

  • Scalable services

  • On demand usage

  • Pay as you go/business model

  • Geodiversity

Distributed clouds challenges

Resource Allocation in Cloud Computing

Distributed Clouds: Challenges

Resource modeling

Resource Allocation in Cloud Computing

Resource Modeling

How can the infrastructural resources be represented?

  • Optimization algorithms depend on it

  • Must take into account both physical and virtual capabilities

  • Two Main Challenges

    • Granularity of Resource Description

    • Interoperability

Granularity of resource description

Resource Allocation in Cloud Computing

Granularity of Resource Description

  • How much detail should be used to describe cloud resources?

    • Less details = less time spent optimizing

    • More details = more flexibility/leverage of resources


Resource Allocation in Cloud Computing


How do you communicate between different/external clouds?

  • Vertical – Applications should interact in a standard way regardless of underlying cloud

  • Horizontal – Different clouds should be able to interact with one another

    • Benefit: “borrowing” resources from one another

Resource offering treatment

Resource Allocation in Cloud Computing

Resource Offering & Treatment

  • Not necessarily offered using the same metrics as internal modeling

    • Example: Internally see 100 individual GB of memory, externally offer one 100 GB chunk

  • Generic

    • Serve as many apps as possible

  • Easy for users to deal with (while still maximizing utilization)

Resource offering treatment1

Resource Allocation in Cloud Computing

Resource Offering & Treatment

  • Must be able to control all resources in cloud

    • Currently implemented by each provider individually

    • Need common protocol for resource reservations in heterogeneous distributed clouds

  • Example criteria

    • Topology

    • Jurisdiction

    • Node proximity

    • Application interaction

    • Scalability

Resource discovery and monitoring

Resource Allocation in Cloud Computing

Resource Discovery and Monitoring

  • Resource Discovery – provider has to find appropriate resources

    • Must minimize inter domain traffic

    • Cannot let new overhead impact QoS

Resource discovery and monitoring1

Resource Allocation in Cloud Computing

Resource Discovery and Monitoring

  • Resource Monitoring

    • Balancing act between amount of control overhead and the frequency of refreshing

    • Two main categories

      • Active – nodes autonomously send to central authority

      • Passive – 1+ entities request info from nodes

      • Either/Both categories are valid, just have to keep them in sync with one another

Resource selection optimization

Resource Allocation in Cloud Computing

Resource Selection & Optimization

How do you choose which entities to use?

  • A priori – allocates optimal resources first

  • A posteri – allocates decent resource first then improves


Resource Allocation in Cloud Computing


  • Many challenges... many opportunities

Cloud computing today

Resource Allocation in Cloud Computing

Cloud Computing Today

  • Same concepts apply

Resource allocation in cloud computing


  • Iaasprovides users with infrastructure services such as computation and data storage, heavily dependent upon virtualization techniques.

  • Current IaaS service providers have adopted a user-based service model which creates lack of support for scalable management of users and resources as well as lack of flexible pricing model

  • A domain-based framework has been proposed for provisioning and managing users and resources


  • Iaas components

    • Virtual resources

    • Uniquely identifiable cloud users

    • Role based access control (RBAC) domain

  • RBAC components

  • U = U x D represents the set of domain users associated with domains, and U x ¢ represents the set of cloud users not associated with any domain. P represents the set of permissions to use virtualized resources associated domains

  • - R and S represents the set of roles and sessions, respectively.

  • UA, PA, and RH, representing the relation of user-to-role assignment, permission-to-role assignment, and role hierarchy, respectively. RH is partial order on R, written as ::5.

Approach contd
Approach contd.

  • user: S → U represents a function mapping each session Si to the single user.

  • - roles: S → 2R represents a function mapping Si to a set of roles, where roles is subset of {rl(Ǝr' ≥ r)[(user(Si), r') E UAl} and Si has permissions UrEroles(s;) {pI (Ǝr" ≤ 5 r) [(p,r") E PAl}.

Approach design and implementation
Approach, design and Implementation

  • Domain based Management and Delegations

    • Management thorugh delegation of the administrative functions

    • Allocation of virtualized resources to each domain

Iaas conclusion
IAAS Conclusion

  • A novel approach to managing visualized resources in cloud computing by introducing the notion of domain and injecting a role-based security policy support into IaaS service model.

  • The approach provides benefits such as domain optimized resource management , domain based advanced policy support based on role, domain based security log analysis and better pricing model.

  • Proof of Concept prototype has been implemented by modifying an existing IaaS framework called Eucalyptus.

Resource allocation in cloud computing

Parallel data processing in the cloud
Parallel data processing in the cloud

  • Parallel processing framework

    • Mostly in clusters

    • Now deployed in IaaS

  • Flexible resource allocation in IaaS

    • On-demand, pay as you go

    • Is it utilized? (Our Focus)



- Scheduler knows cloud environment

- Job description specify dependencies

Can we use MapReduce?

- Not suitable

- Can’t shut down idle VMs. Why?

Challenges in cloud
Challenges in Cloud

  • Opaque data locality

    • Where is the data stored?

  • Topology aware scheduling

  • Infer topologies in IaaS?

    • Unclear if it works

  • Network Virtualization

    • Makes life difficult

  • Any solutions?

    • Yes! Nephele…


  • Data processing framework for cloud

  • Master-Worker Pattern

    • Cloud Controller (CC)

      • Manage resources

    • Job Manager (JM)

      • Master

    • Task Manager (TM)

      • Worker

    • Persistent Storage (Amazon S3)

      • Store intermediate data

Input job
Input Job

  • Modeled as DAG (Job Graph)

    • Multiple input, multiple output

    • Explicit communication path

  • Job graph

    • Simple: Task & relationships

    • No explicit parallelization

    • No mapping of Task -> Instance

  • Submit Job graph to JM

Nephele job processing
Nephele Job processing

  • Convert Job graph -> Execution graph

  • 2 levels of details:

    • Abstract:

      • Task-level

      • Instance allocation/de-allocation

    • Concrete:

      • Fine-grained

      • Map subtasks -> instances

  • Execution stages

    • Group vertex (A vertex in Job graph)

    • Stage start -> allocate instances

    • Send subtasks to TM

    • Store intermediate results to persistent storage

Nephele job
Nephele Job

  • Channel types

    • Network (same stage, different VMs)

    • In-memory (same stage, same VM)

    • File (across stages, persistent storage)

  • Dynamic Resource allocation

    • Delete instances no longer required

    • Allocate instances based on job requirements

    • Reuse instance from previous stage

  • Is the “scheduler” really “dynamic”?

    • Dynamic resource allocation only

    • Job is fixed throughout the execution


  • First to exploit dynamic resource allocation by IaaS

  • Current focus= minimize processing time or cost

  • Improves resource utilization => less cost

  • Savings in cost & time marginal, but grow by size of input data set

  • Open Issues (What can you do more?)

    • Adapt to resource overload or underutilization during job execution automatically

Dynamic optimization of multiattribute resource allocation in self organizing clouds
Dynamic optimization of multiattribute resource allocation in Self-Organizing Clouds

  • VM technology and Resource Multiplexing

  • Self Organizing Cloud(SOC)

    • Large number of desktop computers

    • P2P Network

    • Each participant is Provider & Consumer

    • Autonomous location of nodes

  • 2 Issues (Our focus)

    • Multi-attribute range query

    • Optimize execution time by determining optimal share of resources

Problem formulation
Problem Formulation in

  • Simple Monetary model to analyze economic implications

  • Total payment is given by->

  • where Δt is execution time of tij on ps

  • Given submitted task tij with designated e(tij) and w(tij)

    • 1. Efficiently locate a qualified node

      • In large peer-to-peer network

      • Controlled message delivery

    • 2. Optimize tij‘s execution time ->

Optimal resource allocation
Optimal Resource Allocation in

  • Lemma 1.The optimal allocation (denoted by r*(tij)) exists iff Inequalities (6) and (7) are met

  • Proof: Read on your own 

  • Assume the qualified node ps that satisfies above inequalities can be found by resource discovery protocol (To be discussed later)

  • More math in the paper to calculate the optimization function

    • Out of the scope of our discussion

Optimal resource allocation1
Optimal Resource Allocation in

  • - a(tij) is a “firm” bound – r(tij) cannot be greater than a(tij)

  • - e(tij) is a “soft” bound – r(tij) can be lower than e(tij) because e(tij) is just an estimate by user

  • - To optimize resource allocation without exhausting all possible combinations, relax (5) to (11)

  • - Let denote a subset of

  • resource attributes π

  • - C be budget

  • - CO-STEP( ,C) computes optimal

  • resource vector for tij w.r.t

Optimal proportional share allocation
Optimal Proportional-Share Allocation in

  • Dynamic Optimal Proportional Share (DOPS)

    • Resource Allocation method

    • Leverages Proportional Share Model (PSM)

    • 2 Main procedures (handlers)

      • Slice Handler

      • Event Handler

      • Performed by a node’s VMM

    • Slice Handler

      • Activated periodically

      • Equally scale resource allocation to tasks

      • So each task can acquire additional resources proportional to their demand

    • Event Handler

      • Activated only on Task arrival and completion

      • Redistributes resource

Pointer gossiping can pg can
Pointer-Gossiping CAN (PG-CAN) in

  • Resource Discovery Protocol

  • Content Addressable Network (CAN) as DHT Overlay

  • Duty Node (responsible for a multidimensional range zone)

Pointer gossiping can pg can1
Pointer-Gossiping CAN (PG-CAN) in

  • Periodically propagate state-update to duty node (flooding)

    • Inefficient

    • Search area may be too large

  • Improvement

    • Periodically diffuse a few “pointer” messages to distant nodes (like breadcrumbs)

    • In negative direction

    • 2k hops where k = 0, 1, 2,…

  • Two ways

    • Spreading

    • Hopping

    • Hopping is better

Contributions in

  • Optimize task’s resource allocation under user’s budget

  • Maximize resource utilization based on PSM

  • Lightweight resource query protocol with low contention

  • What next?

    • Fault tolerance support

Resource allocation in cloud computing

Objectives in

  • Proposal of a deadline and budget constrained cost-time optimization algorithm for scheduling dependent subtasks with considerations of communications between them

  • Design for an evolutionary mechanism, which changes multiplexed strategies of the initial optimal solutions of different participants with minimizing their efficiency losses

  • Demonstration of Nash equilibrium in existence in the resource allocation game with the specialized settings.

Scheduling algorithms
Scheduling algorithms in

  • Many scheduling algorithms, but independent.

  • Dependent are NP complete.

  • Heuristic algorithms provide near optimal solutions

  • Lack of practicable solutions due to QoS constraints

  • Game theory based method to schedule dependent tasks with time and cost constraints

Problem modeling 1 9
Problem Modeling 1/9 in

  • n tasks share m computational resources

  • Task S(i) k(1) (communication makes

  • k(2)…. things complicated)

  • k(i)

  • k(i) parallel and dependent subtasks

  • Resource Rj -> Price Pj-> Capacity

  • Rj

  • K(1) k(2) k(3) k(4) k(5) k(6)…. k(n) (Subtask)

  • Cj/n Cj/n Cj/n Cj/n……………...Cj/n (Capacity)

  • Pj/n Pj/n Pj/n Pj/n……………...Pj/n (Price)

Problem modeling 3 9
Problem Modeling 3/9 in

  • Tij : Turnaround time for resource Rj to complete task aij subtasks of task Si

  • Eij : Expense Task Si pays to resource Rj to complete aij subtasks

  • Wt : weight of completion time

  • We: weight of expense

  • Total Cost:

  • Total Utility:

  • Aij: Amount of subtask k(i) of task Si allocated to resource Rj

Problem modeling 8 9
Problem Modeling 8/9 in

  • Above problem is NP- Complete

  • Approximate solution can be found using following steps:

  • 1) Independent problem solving by participant

  • for

  • 2) Evolutionary mechanism changing multiplexing strategy of initial optimal solutions

Nash equilibrium
Nash Equilibrium in

  • Ingame theory, the Nash equilibrium is a solution concept of anon co-operative involving two or more players, in which if each player has chosen a strategy(common knowledge) and no player can benefit by changing strategies while the other players keep theirs unchanged, then the current set of strategy choices and the corresponding payoffs constitute a Nash equilibrium

  • If final evolutionary solution satisfies the constrained conditions of all tasks, it forms a Nash equilibrium of the resource allocation game.

Experiment in

  • Step 1: Find valid SPELR and GELR. If not found, exit.

  • Step 2: Perform reallocation and compute new utilities for all tasks.

  • Step 3: Go to step 1. Repeat reallocating till no valid SPELR and GELR is found.

Performance evaluation
Performance Evaluation in

  • Initial Optimization:

  • Binary Integer Programming- O(mn^2)

  • Evolutionary Optimization – O(mnlogm)

  • Serial processing of algorithms

  • Overall time complexity

  • max{O(mn^2), O(mnlogm)}

Resource allocation in cloud computing

  • On the fly Resource in Allocation

  • Dynamic Resource Allocation in Computing Clouds through Distributed Multiple Criteria Decision Analysis

  • -Prasanna

Introduction in

  • The ability to migrate VMs makes it possible to dynamically adjust data center utilization and tune the resources allocated to AEs.

  • These adjustments can be automated through formally defined strategies in order to continuously manage the resources in a data center with less human intervention.

  • Adopts a distributed architecture where resource management is decomposed into independent tasks, each of which is performed by Autonomous Node Agents that are tightly coupled with the physical machines in a data center.

  • The Autonomous Node Agents carry out configurations in parallel through Multiple Criteria Decision Analysis using the PROMETHEE method.

Conclusion in

  • The approach consists of a distributed architecture of NAs that perform resource configurations using MCDA with the PROMETHEE method. The simulation results show that this approach is promising particularly with respect to scalability, feasibility and flexibility.

  • Scalability is achieved through a distributed approach that reduces the computational complexity of computing new configurations.

  • Feasibility is due to the significantly lower number of migrations that are performed in order to apply new configurations.

  • The flexibility of our approach comes from its ability to easily change the weights of criteria and adding/removing criteria in order to change configuration goals.

Resource allocation in cloud computing

  • SLA in

  • Resource allocation policies in autonomic environments

Overview in

  • Introduction : Need for autonomic resource allocation management based on current IT industry trends.

  • SLA i.e service level agreement are used in the industry as an utility which determines costs and penalties on the basis of performance level in overall IT infrastructure.

  • The overall system could been abstracted into high-level components, a monitor, a predictor and a resource-allocator

  • This presentation is based on the paper which focuses on the design of a resource allocator for autonomic multi-tier environment. The goal is to maximize revenue while balancing the cost of using the resources.

  • The problem has been modeled as a mixed non-linear programming problem and heuristic solutions has been developed based on a local search approach.

  • The neighborhood approach is based on a Fixed-point iteration(FPI) technique, which iteratively solves a scheduling and a load balancing problem by implementing a gradient method.

  • The model considers jointly a broader set of control variables and uses mathematical based programming techniques to obtain a solution of the problem. Benefits have been shown at the end of the presentation based on the experimental results.

Related literatures
Related Literatures in

  • The SLA maximization problem considers

  • (i) the set of servers to be turned ON depending on the system load

  • (ii) the application tiers to servers assignment

  • (iii)the request volumes at various servers and

  • (iv) the scheduling policy at each server as joint control variables.

  • In one paper, authors address the problem of handling service centers resources in overload conditions while maximizing SLA revenues. But the applications are assigned to dedicated servers and load balancing problem is not addressed.

  • Another author considers the optimization of multi-tier system where the system workload is evenly shared among servers and the processor sharing (PS) scheduling policy. But control variables in (iii) and (iv) are not taken into account. A single class model is considered and the problem is solved by enumeration.

Related literatures contd
Related Literatures contd. in

  • The problem of minimization of system response time and maximization of throughput is analyzed. The work proposes a static algorithm which assigns Websites to overlapping servers executed once a week, on long term predictions basis, while a dynamic algorithm implements a real time dispatcher and assigns incoming requests to servers considering short term loans.

  • Another paper uses continuous utility functions where the load is evenly shared among servers and the problem of maximization of SLA is formulated as a scheduling problem. The effectiveness is verified using simulation.

  • In another paper the authors faced dual problem of minimizing customers discontent function considering an online estimate of service time requirements and their response times. The optimal generalized processor sharing (GPS) is identified by using Lagrange techniques

  • Finally, in a previous paper, the author extended work by considering step-wise utility functions and the number of servers to be switched ON as a control variable. The pricing schema considered the response time of each request at a single tier. In multi-tier system, the flexibility provided by current technology can be exploited to implement the most convenient load sharing among multiple machines, if the overall resource allocation problem is addressed.

System model1
System Model in

  • -The overall system is modeled by a queuing network composed of a set of multi-class single server queues and a multi- class infinite server queue. The first layers of queues represent the collection of physical servers supporting requests execution. The infinite server queues represent the client based delays, or think time, between the server completion of one request and the arrival of the subsequent request within a session.

  • - User session begins with a class k request arriving to the service center from an exogenous source with rate λK rate The analysis of actual e-commerce site traces showed that the Internet workload follows a Poisson distribution,hence it has been assumed that the exogenous arrival streams are Poisson processes. Upon completion the request either returns to the system as a class k request with probability pk,k' or it completes with probability 1 −Σ(l=1,k) Pk,l

  • - Let Λk denote the aggregate rate of arrivals for class k requests, than

  • Λk =Σ(k'=1,k) Λk'Pk,'k + λK

System model contd
System Model contd. in

  • The service center can be characterized by the following set of parameters :

  • K := set of request of classes;

  • Nk:= number of application tiers involved in the execution

  • of class k requests;

  • M := number of physical servers at the service center;

  • Ci:= capacity of physical server i;

  • ci := time unit cost for physical server iON;

  • Ai,k,j:= 1 if physical server ican support the execution of

  • application tier j for class k request, 0 otherwise;

  • k,j:= maximum service rate of a capacity 1 physical server

  • for executing processes at tier j for class k requests.

  • Here, different request classes require different number of application tiers Nk to be executed. For example a request of static web page is executed by a web server , while the request of dynamic web-page involves multiple tiers, from the web server to the DBMS tier.

System model contd1
System Model contd. in

  • The decision variables of our model are the followings:

  • xi := 1 if physical server iis ON, 0 otherwise;

  • zi,k,j:= 1 if the application tier j for class k requests is assigned to physical server i, 0 otherwise.

  • i,k,j:= rate of execution for class k requests at application tier j on physical server i;

  • i,k,j:= GPS parameter at physical server ifor executing application tier j for class k requests.

  • The analysis uses GPS bounding technique to approximate the queuing system.

  • Request at different application tiers within each class and on every physical server are executed in FCFS or a PS manner.

  • The objective is to maximize the difference between revenues from SLAs and the cost associated with physical servers ON in the inter-scheduler time period which can be expressed as Σ(l,K)Λk

Optimization technique
Optimization Technique in

  • The optimization problem can be solved by considering heuristic approach.

  • Application tiers capacity estimation

  • The optimization problem can be optimized as:

Optimization technique1
Optimization technique in

  • Application tiers to servers assignment

Optimization technique2
Optimization Technique in

  • Load balancing and scheduling problem can be modeled as follows:

Conclusion in

  • An allocation controller is proposed for multi-application tier service center environments which maximizes the profits associated with multi-class SLAs.

  • The cost model consists of a class of utility functions which includes revenues and penalties incurred depending on the achieved level of performance and the cost associated with physical servers.

  • The overall optimization problem considers a set of physical servers to be turned ON, the allocation of applications to physical servers and load balancing and scheduling at physical servers as joint control variables, is NP-hard. But a heuristic solution was developed using local search algorithm.

  • Experimental results, up to 400 physical servers and 200 request classes, show that revenues that can be obtained with a PAS can be significantly improved and important savings can be obtained on light and medium load conditions.

Resource allocation in cloud computing

Resource Allocation in Cloud Computing in

Resource Pricing


Equilibrium Allocation Policy

Resource pricing and equilibrium allocation policy

Resource Allocation in Cloud Computing in

Resource Pricing and Equilibrium Allocation Policy

What about the $$$?

  • Users who pay more should get more service

  • Previous algorithms have maximized resources

  • Need a way to maximize profit and usability

Market oriented allocation policy

Resource Allocation in Cloud Computing in

Market Oriented Allocation Policy

  • Adds supply and demand to the equation

  • Existing products

    • G-commerce

    • BEinGRID

    • GridEcon

    • Cloudbus

    • Good products, but fail to utilize competition between consumers ($$$ opportunity)

Algorithmic considerations

Resource Allocation in Cloud Computing in

Algorithmic Considerations

  • In auctions, one user can't know what the other user will bid

  • Pay-as-you go means bidding is dynamic

  • Users can adjust bids based on others behavior

  • Realistic budget/time constraints to keep the math in scope.


Resource Allocation in Cloud Computing in


  • α: remaining capital for current task

  • β: minimum time to finish task

  • γ: time to finish the leaving tasks in job seq.

  • ω: flextime under deadline

  • α/β: available budget for current task

Communication flowchart

Resource Allocation in Cloud Computing in

Communication Flowchart

Algorithm steps

Resource Allocation in Cloud Computing in

Algorithm Steps

  • Users each ask auctioneer about config info

  • Auctioneer responds with info

  • Users each bid based on their value of resources

  • Auctioneer responds with sum of all bids

  • Users each calculates α, β, γ, and ω and send info back to auctioneer

  • Auctioneer determines final resource price and allocation

Turns out it works

Resource Allocation in Cloud Computing in

Turns out it works!

  • Using both budget and time as constraints, the price eventually equalizes

Future considerations

Resource Allocation in Cloud Computing in

Future Considerations

  • Current algorithms are...

    • Very general

    • Restrained in variables considered

  • Other economics non-cs considerations that can be included


Resource Allocation in Cloud Computing in


  • Resource allocation is an important aspect of cloud computing.

  • Many different algorithms that cover many different aspects.

  • One big balancing act

Questions? in

  • Distributed Clouds and their Challenges

  • IaaS

  • Resource Allocation based on pre-known

  • demands

  • On the fly Resource Allocation

  • SLA

  • Resource Pricing and Equilibrium Allocation Policy

Resource Allocation in Cloud Computing