The Resource-as-a-Service (RaaS) Cloud Orna Agmon Ben-Yehuda Muli Ben-Yehuda Assaf Schuster Dan Tsafrir Department of Computer Science Technion — Israel Institute of Technology HotCloud 2012 Agmon Ben-Yehuda, Ben-Yehuda, Schuster, Tsafrir Resource-as-a-Service (RaaS) 1/18
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The Resource-as-a-Service (RaaS) Cloud
Orna Agmon Ben-Yehuda Muli Ben-YehudaAssaf Schuster Dan Tsafrir
Department of Computer ScienceTechnion — Israel Institute of Technology
Most cloud providers sell fixed bundles, called “instancetypes” or “server sizes”.
Amazon allows adding and removing of “networkinstances” and “block instances”, thus dynamicallychanging I/O resources.CloudSigma offers clients to compose a flexible bundle.
As physical servers increase, an entire server may be toomuch for a single client.
Renting a fixed bundle may waste client resources, even ifits requirements stay the same over time. For example, ifthe client can only use 7 cores, why should it rent 8?
We extrapolate that clients will rent a basic bundle, anddynamically supplement it with resources in fine granularity.
If only the first two trends culminate as described, then clientscan finally optimize their resource use.However, this is not enough to guarantee a green, efficientcloud. Would they really optimize? Will they optimize the righttarget function for a green cloud?
Benchmarks show great variance in the performance ofsupposedly similar cloud instances.Different clients need different guarantees: a bank will payfor 100% availability. A small business may settle for a 95%guarantee.Client valuations of performance and resources differ andare private information.Some researchers (Padala’09, Heo’09, Nathuji’10) arguefor selling client performance and measuring it. Thisconcept is impossible for a real commercial IaaS black boxclient.
IaaS Providers cannot sell performance. They must keepselling resources.
Both clients and providers must continuously decide whatto buy and when to buy it.The fine rent time granularity and bundle flexibility makesdecision making a core function.Both providers and clients will use economic agentsoftware to handle decision making and economicinteraction.
Has a view of the global picture (total system resources,change predictions)
Dictates economic mechanisms and protocols.
Allocates resources according to agreements.
Uses the resources to verify that high-QoS clients aresatisfied, possibly at the expense of low-QoS clients on thesame machine, and given the specific current needs ofeach client.
Economic (game theoretic) mechanisms for multi-resourceallocation with different QoS levels.
RealisticIncentive compatibleCollusion-resistantComputationally efficient at large scaleOptimizes the provider’s revenue or a social welfarefunctionMinimizes the price of anarchy