Jalal Khamse-Ashari

Efficient and fair scheduling of placement constrained threads on heterogeneous multi-processors

Cloud computing platforms are increasingly deploying multi-processors that are heterogeneous in the resource capacities or functionality of their processors (Instruction Set Architecture, or ISA). ISA heterogeneity (e.g., CPU vs GPU) or administrative policies can additionally create placement constraints whereby certain threads may only execute on a subset of the available cores. Fair CPU scheduling in such settings poses novel challenges that we address in this paper. First, we describe the conditions for a feasible allocation. We then develop a general utility optimal scheduling framework that, when appropriately parameterized, adjusts the trade-off between fairness and throughput, and captures a variety of notions of fairness (proportional fair, max-min fair, etc.). Finally, we design a low-complexity quantum-level scheduling algorithm, called CMFS. We evaluate the efficacy of CMFS via simulations and identify promising future directions.

Constrained max-min fair scheduling of variable-length packet-flows to multiple servers

In this paper, we study a multi-server queuing system wherein each user is constrained to get service only from a specified subset of servers. Fair packet scheduling in such a setting poses novel challenges that we address in this paper. Specifically, we observe that max-min fair allocation of the available resource over different servers (notably bandwidth) in the presence of placement constraints results in different levels of fair service-rates. To achieve the max-min fair service rates, we propose a novel packet scheduler which is inspired by the deficit-round robin (DRR) algorithm. The scheduler allocates tokens to flows in a round-by-round manner, where token allocation to flows at the beginning of each round is weighted max-min fair. So, we have called it multi-server max-min fair DRR (MSMF-DRR). The performance of the MSMF-DRR algorithm in terms of achieving fairness is shown through a worst-case performance analysis. In addition to analytical results, numerical experiments are also carried out to illustrate service isolation and the delay guarantee that are provided by the algorithm. Generally, a scheduler for such a constrained multi-server queuing system can be applicable in many modern data-networking applications, especially in cloud computing wherein virtual machines and/or processes vie for different IT resources distributed over heterogenous servers, while different processes may have preferences over servers owing to their quality-of-service requirements and the heterogeneity of servers.

Constrained Max-Min Fair Scheduling of Variable-Length Packet-Flows to Multiple Servers

We describe a scheduler for multiple servers shared among different packet-flows, where each packet-flow may be served by only a subset of available (preferred) servers. The scheduler allocates tokens to flows in a round-by-round manner, where token allocation to flows at the beginning of each round is weighted max-min fair. We present a packet scheduling scheme where when a server becomes free, it is allocated to serve the HOL packet of an eligible flow with the maximum remaining tokens. The scheduling algorithm is applicable even when the capacity of servers are not known a priori and may vary over duration of a round. Numerical examples are given to illustrate that the scheduler itself is weighted max-min fair.