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Intelligent Workload Factoring for A Hybrid Cloud Computing Model

www.nec-labs.com. Intelligent Workload Factoring for A Hybrid Cloud Computing Model. Hui Zhang Guofei Jiang Haifeng Chen Kenji Yoshihira Akhilesh Saxena NEC Laboratories America Princeton, NJ July 10 th , 2009. IT trends: Internet-based services and Cloud Computing.

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Intelligent Workload Factoring for A Hybrid Cloud Computing Model

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  1. www.nec-labs.com Intelligent Workload Factoring for A Hybrid Cloud Computing Model Hui Zhang Guofei Jiang Haifeng Chen Kenji Yoshihira Akhilesh Saxena NEC Laboratories America Princeton, NJ July 10th, 2009

  2. IT trends: Internet-based services and Cloud Computing • Trend on IT infrastructure • Adoption of cloud computing architecture. • Computations return to the data centers. • Promise of management simplification, energy saving, space reduction, … • Trend on IT applications • Adoption of service oriented architectures & Web 2.0 applications, e.g. • Software as a Service (SaaS) • Mobile commerce • Open collaboration • Social networking • Mashups Public clouds Private clouds Blue Cloud Google applications

  3. What is Cloud Computing? • An emerging computing paradigm • Data & services : Reside in massively scalable data centers • Can be ubiquitously accessed from any connected devices over the internet. • The unique points to cloud computing users are the Elastic infrastructure and the Utility model: provision on demand, charge back on use. [IBM] Businesses, from startups to enterprises Web 2.0-enabled PCs, TVs, etc. 4+ billion phones by 2010 [Source: Nokia]

  4. Cloud Computing is not a reality yet for the majority • “Little Investment In Cloud & Grid Computing for 2009.” • “CIOs are looking primarily to tested, well-understood technologies that can result in savings or increased business efficiencies whose support can be argued from a financial point of view” • a survey by Goldman Sachs & Co., July 2008. • What about current application platform? • What about data privacy? • What about the performance? • Why the full package? • …. Private cloud? Public cloud? Choose one, please! Let me think about it.

  5. IT customers can have the best Total Cost of Ownership (TCO) strategy with their applications running on a hybrid infrastructure Local data center, small and fully utilized for best application performance. Remote cloud, infinite scaling, use on demand and pay per use. A hybrid cloud computing infrastructure model Remote cloud (large, pay per use) 5% workload, 1% time User requests Workload factoring User requests Dynamic Workload 95% workload, 100% of time Local data center (small, dedicated) 5

  6. The economic advantage of hybrid cloud computing model: a case study Annual Cost ($$) Hosting solution † Cost on running a 790-servers data center A local data center hosting 100% workload To host Yahoo! Video website workload ‡ Amazon EC2: peak workload of 5% time US $ 7.43K + + Cost on running a 99-servers data center A local data center: workload of 95% time Workload Factoring † ‡ Amazon EC2 hosting 100% workload US $ 1.384M †: assume over-provisioning over the peak load ‡: only consider server cost. Amazon EC2 pricing: $0.10 per machine hour – Small Instance (Default). 6

  7. Hybrid Cloud Computing architecture (1) (2) (3) Design goals smoothing the workload dynamics in the base zone application platform and avoiding overloading scenarios through load redirection; making trespassing zone application platform agile through load decomposition not only on the volume but also on the application data popularity.

  8. Intelligent workload factoring: problem formulation • Solution: • fast data frequency estimation • Graph model generation • greedy bi-section partition • Hypergraph partition [Karypis99] • Problem statement: • Input: • requests (r1, r2, …, rM). • data objects (d1,d2, …,dN). • request-data relationship types (t1=(di,dj,…), t2=(dx,dy,…),…, tR) • each request belongs to one of the R types • Output: • Request partition schemes (R1, R2,…, RK) and data partition schemes (D1,D2,…,DK ) for K locations. • Problem: a fast online mechanism to make the optimal decision on request and data partition for minimal cross-location data communication overhead. Loc. 2 Loc. 1 d4 d1 d3 d6 d5 d2 A hypergraph partition problem model (NP-hard) Subject to Where: request type i; # of requests for type-i; sum of the vertex weights in Location-k Loc-i capacity of res. type t (1: storage, 2: computing)

  9. The fast top-k data item detection algorithm Time t0 Data popularity Pold Data popularity Pnew • Design goal • Starting at t0, reach an estimation accuracy on the top-k data items in Pnew within the minimal time. • The key ideas leading to the detection speedup • filtering out old popular data items in a new distribution • filtering out unpopular data items in this distribution.

  10. Speedup analysis of the fast top-k algorithm • Problem model • Formally, for a data item T, we define its actual request rate p(T) = total requests to T/total requests . • FastTopK will determine an estimate p’(T) such that with probability greater than α. • We use Zα denote the percentile for the unit normal distribution. For example, if α = 99.75%, then Zα = 3. • Main speedup result • Define an amplification factor X for the rate change of a data item before and after the historical topk-K filtering as • Theorem 1: LetNCbefore be the number of samples required for basic fastTopK, and NCfafter be the number of samples required for filtering fastTopK • Notation: X2 speedup of the detection process even with a X-factor on rate amplification due to historical information filtering.

  11. Fast and memory-efficient workload factoring scheme Arriving request Panic mode? n y Fast top-k data item detection scheme “Base zone” Does it belong to the top-k list? n end y “Trespassing zone” “Base zone” end end

  12. A complete request dispatching process in hybrid cloud computing Arriving request Workload factoring Base zone Trespassing zone Workload shaping Round-robin dispatching admit drop Drop the request LWL Available server? n y end end Admit the request Drop the request end end

  13. Testbed setup IWF a http request load controller request forwarding Dispatching decision http reply S3 EC2 rtsp://streamServer_x//… rtsp://streamServer_x//…

  14. Workload factoring evaluation: incoming requests t0

  15. Workload factoring evaluation: results (I)

  16. Workload factoring evaluation: results (II) Base zone server capacity Trespassing zone server capacity

  17. Conclusions • We present the design of intelligent workload factoring, an enabling technology for hybrid cloud computing. • Targeting enterprise IT systems to adopt a hybrid cloud computing model where a dedicated resource platform runs for hosting application base loads, and a separate and shared resource platform serves trespassing peak load of multiple applications. • The key points in our research work • Matching infrastructure elasticity with application agility is a new cloud computing research topic. • Workload factoring is one general technology in boosting application agility. • CDN load redirection is a special case.

  18. Backup slides

  19. Multi-application workload management Multi-application workload management architecture

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