Bestavros, Azer

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1Network and Cloud Resource Packing GamesAzer BestavrosComputer Science DepartmentBoston UniversityJoint work with Jorge Londono (BU?U Pontificia Bolivariana), Vatche Ishakian (BU), Ray Sweha (BU),Nikos Laoutaris (BU?Telefonica), and Shanghua Teng (BU?USC)http://www.cs.bu.edu/groups/wingTechnical University of Berlin & Deutsche Telekom LabsJune 22, 2010Pay as you go + Autonomy = Market? Not your father’s Internet ? Tenants make resource acquisition/control decisions and have no incentive to optimize for, or be fair/friendly to others? Infrastructure owners have no incentive to minimize cost for tenants ? Net neutrality implies no policing June 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 2? Holistic system (social) view is passé? Challenge is to design the right mechanisms that enable an efficient marketplaceResource Packing Problems? Spatial Packing (where)? Bin Packing? Load Balancing? Multiprocessor scheduling? Temporal Packing (when)? Statistical Multiplexing? Real Time SchedulingResource InstancesLoaddJune 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 3-? Traffic Shaping? Spatio-Temporal Packing (both)TimeLoadTalk overview: Two settingsCloud resource acquisition ? Colocation GamesA spatial packing gameShared bandwidth arbitration? Trade & Capl kResource InstancesLoaddJune 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 4A tempora  pac ing gameTimeLoad2Spatial Packing: Network EmbeddingHostJune 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 5http://csr.bu.edu/netembedTaskMotivation: IaaS pricing “Pricing is per instance-hour consumed for each instance type. Partial instance-hours consumed June 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 6are billed as full hours.”(Cloud) Colocation Games? IaaS cloud providers offer fixed-sized instances for a fixed price? Provider’s profit = number of instances sold; no incentive to colocate customers? Virtualization enables colocation to reduce costs without QoS compromises? C t ’ lfi h  d  th  June 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 7us omers se s ness re uces ecolocation process to a strategic gameColocation Games08:00 am / Amazon ? $3 09:00 am / Amazon ? $3Tasks10:00 am / Amazon ? $2 11:00 am / Amazon ? $2HostsJune 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 83Colocation Games: Questions? Does it reach equilibrium?? If so  how fast? ,? If so, at what price (of anarchy)?? How about multi-resource jobs/hosts?? How about multi-job tasks?? How about job/host dependencies?June 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 9? How could it be implemented?? How would it perform in practice?How do we depart from prior work?? Vickrey-style auctions work†? Assumes supply < demand ? Takes a social perspective ? Offers a strategy-proof solution? Requires central authority? Susceptible to collusionJune 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 10† A. Young, B. Chun, A. Snoeren, and A. Vahdat. Resource allocation in federateddistributed computing infrastructures. In OS/architectural support for on-demand ITinfrastructure, 2004.How do we depart from prior work?? Cooperative cost-sharing games†‡#? Find coalition where nobody gains by leaving? Computationally hard? Applied to best-effort routing problems? Player cost not use based; unjustifiable June 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 11† V. Misra, S. Ioannidis, A. Chaintreau, and L. Massoulié. Incentivizing Peer-AssistedServices: A Fluid Shapley Value Approach. In SIGMETRICS 2010.‡ H. Chen and T. Roughgarden, T. Network design with weighted players. In SPAA 2006. # E. Anshelevich, A. Dasgupta, J. Kleinberg, E. Tardos, T. Wexler, and T. Roughgarden.The price of stability for network design with fair cost allocation. In FOCS 2004.Colocation Game: Model ? A hosting graph G =(V,E)? V & E labeled by capacity vector R and fixed price P? A set of task graphs Ti =(Vi,Ei)? Vi & Ei labeled by a utilization vector W? Valid mappings? Vi ? V & Ei ? E: Σ W ≤ R ; supply meets demand? Shapley Cost functionJune 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 12? Cost P of a resource is split among tasks mapped to it in proportion to use4The General Colocation Game (GCG)? GCG is a pure strategies game: Each task is able to make a (better response) “move” from a valid mapping M into another M′ so as to minimize its own cost? Example applications:? Overlay reservation, e.g., on PlanetLabJune 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 13? CDN colocation, e.g., on CloudFrontGeneral Colocation Game: Properties ? GCG may not converge to a Nash equilibrium? Theorem: Determining whether a GCG has a Nash Equilibrium is NP-Complete (by reduction to 3-SAT problem)June 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 14? Need more structure to ensure convergenceColocation Games: Variants? Process Colocation Game (PCG):Task graph consists of a single vertex representing an independent process that needs to be assigned to a single host with only one capacitated resource? Multidimensional PCG (MPCG):Same as PCG but with multi capacitated resourcesJune 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 15? Example applications:? VM colocation, e.g., on a Eucalyptus cluster? Streaming server colocationColocation Games: Variants? Parallel PCG (PPCG):Task graph consists of a set of disconnected vertices (independent processes), each with multidimensional resource utilization needs? Uniform PPCG:Same as PPCG but with identical resource utilization for all processesJune 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 16? Example applications:? Map-Reduce paradigm? MPI scientific computing paradigm5Colocation Games: Theoretical results? PCG converges to a Nash Equilibrium under better-response dynamics? PCG converges to a Nash Equilibrium in O(n2)better-response moves, where n = |V| ? Price of Anarchy for PCG is 3/2 when hosting graph is homogeneous and 2 otherwise? MPCG converges to a Nash equilibrium under better response dynamicsJune 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 17-? Uniform PPCG converges to a Nash equilibrium under better-response dynamics? …PCG: Better ResponseBest-Response moves require knowledge of utilizations of all processes – not practical Local Better-Response solution:1. Select a random target hosting node and obtain process utilizations of all processes on that node2. Determine if a cost-reducing “legal” move to that node is possible – an NP-hard Knapsack problem June 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 18? Dynamic Programming solution in pseudo-polynomial time for small number (100s) of processes/host [DPKP] ? Breadth-First branch & bound Search heuristic [BFS] ? Depth-First branch & bound Search heuristic [DFS] PCG: Performance EvaluationWorkloads? Trace-driven: CoMon PlanetLab traces? Real hosting environment with 3-dimensional resource utilizations? Infeasible to compute optimal colocation? Synthetic? Allows systematic exploration of the space? Optimal colocation is known by constructionMetrics (over 100 experiments)June 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 19? Colocation Ratio (bounded by PoA)? How inefficient is the resulting colocation compared to optimal or best? ? Number of moves (not migrations) until NE is reached? How much churn (overhead) to be expected?PCG: Synthetic baseline resultsMedian(Colocation Ratio) Median(Number of Moves)1-D Synthetic WorkloadPoA    June 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 20Number of processes Number of processes6MPCG: PlanetLab baseline resultsMedian(Colocation Ratio) Median(Number of Moves)3-D PlanetLab WorkloadJune 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 21Number of processes Number of processesMPCG: Colocation RatioMedian(Worst/Optimal) Median(Worst/Best)3-D Synthetic Workload 3-D PlanetLab WorkloadJune 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 22Number of processes Number of processesMPCG: Number of MovesMedian(Number of Moves) Median(Number of Moves)3-D Synthetic Workload 3-D PlanetLab WorkloadJune 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 23Number of processes Number of processesThe CLOUDCOMMONS prototype? API for Strategic ServicesTo facilitate colocation, e.g., allow users to find each other,  compute strategic responses, …? API for Operational ServicesTo enforce outcomes of colocation, e.g., migration, reconfiguration, accounting, …June 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 24? Implemented over Xen (XCS)7CLOUDCOMMONS: ArchitectureJune 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 25CLOUDCOMMONS: Migration Service? Identify VMs to migrate? Minimize number of migrations? Minimize amount of data that needs to move? Determine migration plan? Exploit potential for parallelism? Minimize need for staging hosts? E l t  i t f i tiJune 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 26va ua e mpac o m gra on? On performance of the migrating VMs? On performance of non-migrating VMsNeed for staging hostsJune 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 27A temporary (staging) host is needed to swap VM2 and VM4Need for a migration planJune 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 288Data/User Transfer MinimizationJune 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 29Theorem: The DTM (UTM) heuristic results in at most twice the amount of data transfer (migrations) incurred by an optimal (NP-hard) algorithm.Impact on TPC-W from migrationMigrating VM Migrating VMJune 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 30Non-Migrating VM Non-Migrating VMCLOUDCOMMONS: Benefit to usersJune 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 31Planet-Lab trace-driven experiments(Overheads/costs of all XCS services included)Talk overview: Two settingsCloud resource acquisition ? Colocation GamesA spatial packing gameShared bandwidth arbitration? Trade & Capl kResource InstancesLoaddJune 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 32A tempora  pac ing gameTimeLoad9Today’s last mileJune 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 33The perils of the fixed pricing model? It’s here to stay; metered pricing rejected? Implications:? Customer has no incentive to save bandwidth? ISP cost depends on peak demand – 95/5 rule ? Reigning in bandwidth hogs is incompatible with Net NeutralityJune 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 34? Must devise mechanisms that take ISPs out of the “traffic shaping” businessDSLAM “last-mile” architectureBroadband RemoteAccess ServerDSL AccessMultiplexerJune 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 35Traffic shaping done at BRAS   Solution: Create a marketplace? Recognize the two types of user traffic:? Interactive Traffic (IT)? Browsing, VoIP, Video, Messaging, Gaming, …? Limited bandwidth; highly sensitive to response time? Fluid Traffic (FT)? P2P, Network backup, Netflix/software downloads, … ? Open-ended bandwidth; less sensitive to response time June 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 36? Create a marketplace:1. Give users rights to DSLAM bandwidth, and2. Let users trade IT & FT allocations over time10The Marketplace? Each user gets a fixed budget per epoch? Budget proportional to level of service ? An epoch is a fixed number of time-slots, e.g., 1 day = 288 5-min slots? Trade & Cap? User engages in a pure strategies game that June 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 37yields a schedule for its IT sessions? User acquires as much FT bandwidth as its remaining budget would allowTrading Phase: Strategy Space? Session: An IT session is the sequence of slots during which an IT li ti  i  tiapp ca on s ac ve? Slack: User may have flexibility in scheduling IT sessions; slack specifies the number of slots that an IT session is allowed to be shifted back/forthJune 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 38? Strategy Space:The set of all possible arrangements of IT sessions within allowable slack define the strategy space for a userTrading Phase: Cost Function? Let xik be the bandwidth used in slot k by a chosen IT session schedule for user i.? The cost incurred by user i  is given by:∑ ∑∑∈ ∈∈ ⎟⎟⎠⎞⎜⎜⎝⎛=⋅=slotsk usersjjkikslotskkiki xxUxcJune 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 39? Cost of user i  depends on the choices made by other users – hence the game!Trading Phase: IllustrationCost(User 2) = 6User 2 User 2June 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 40User 1 User 1Up 2 2 20 0 01 111Trading Phase: IllustrationCost(User 2) = 4User 2 User 2June 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 41User 1 User 1Up 1 2 11 0 11 1Trading Phase: Best Response? BR of user i is the schedule of IT sessions that minimizes its cost ci ? Computing BR is NP-hard, equivalent to solving a generalized knapsack problem? Dynamic programming solution is pseudo-polynomial in the product of the number of sessions and number of slotsJune 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 42? Scales well for all practical settings –100s of users and 100s of slots Trading Phase: Findings? Provably converges to Nash Equilibrium, even in presence of constraints? For n users, Price of Anarchy is n, but in practice below 2, especially for n>10? Experimentally, large reduction of peak utilization, even with small flexibilityJune 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 43Capping Phase: Best Response? BR of user i is to maximize total FT allocationsubject to the budget constraint∑∈=slotskiki wwBU ⎟⎞⎜⎛∑ ∑June 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 44iislotsk usersjjkpik cww −=⎟⎠⎜⎝+⋅∈ ∈12Capping Phase: Budget? Let V be an upper-bound on traffic per slot? The ISP sets a target capacity C = V/R, where R ≥ 1 reflects its “resistance” to traffic ? The ISP allocates C in some proportion (e.g., equally) to all users over all slots? This constitutes the budget B assigned to a user over an epoch June 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 45TnCB ⋅=Capping Phase: Findings? Computing BR is efficient using Lagrange Multipliers method? Provably, converges to a unique global (social) optimum that maximizes the FT allocations of all users? Experimentally, smoothes the aggregate IT+FT traffic to any desirable level June 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 46controlled by resistance parameter RExperimental EvaluationWorkloadDerived from WAN traces of MAWI project? Identify users from volume and direction of flows to known ports (e.g., most traffic destined to port 80)? Identify user IT sessions using thresholds on per-IP June 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 47traffic intensities over time? Slack introduced using various models (e.g., fixed,  proportional, etc.) Trading Phase: Experimental PoAOver 5 slots Over 10 slotsJune 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 48Theoretical PoA is n but not in practice13Trading Phase: Smoothing effectValue proposition to ISPsMax SlackReduction in 95% 3 15%6 24%12 31%0.95me / SlotDFJune 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 49Traffic VoluCDTime Slot Traffic Volume / SlotTrade & Cap: Flexibility pays off!Value proposition to customersJune 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 50Trade & CapA win-win for ISPs and customersized Traffic VolumeJune 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 51Time SlotNormaliTrade & Cap: Implementation? On Client Side (DSL Modem): + Strategic agent to execute Trade & Cap+ Operational service to classify and scheduleJune 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 52? ISP Side (DSLAM or BRAS):+ Support exchange between strategic agents+ Enforce traffic/slot/user from Trade & Cap14Trade & Cap: ImplementationJune 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 53Conclusion? In many settings, resource management must be seen as a strategic game among peers or tenants of an infrastructure? By setting up the right mechanism, one can ensure convergence and efficiencyJune 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 54? New services are needed to support strategic and operational aspects of these game-theoretic mechanisms Publications“netEmbed: A service for embedding distributed applications (Demo)”. Londono and Bestavros. ACM/Usenix Middleware’07.“netEmbed: A resource mapping service for distributed applications”. Londono and Bestavros  IEEE/ACM IPDPS’08. .“Colocation games with application to distributed resource management”. Londono, Bestavros, and Teng. USENIX HotCloud'09.“Colocation as a Service: Strategic & operational cloud colocation services”. Ishakian, Sweha, Londono, and Bestavros. IEEE NCA’10. “Trade & Cap: A customer-managed system for trading bandwidth at a shared link”. Londono, Bestavros, and Laoutaris. BUCS-TR-2009-025.June 22, 2010 Network and Cloud Resource Packing Games @ TU Berlin 55Network and Cloud Resource Packing GamesAzer BestavrosComputer Science DepartmentBoston UniversityJoint work with Jorge Londono (BU?U Pontificia Bolivariana), Vatche Ishakian (BU), Ray Sweha (BU),Nikos Laoutaris (BU?Telefonica), and Shanghua Teng (BU?USC)http://www.cs.bu.edu/groups/wingTechnical University of Berlin & Deutsche Telekom LabsJune 22, 2010

Network and cloud resource packing games

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Network and cloud resource packing games

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