Locality-Aware Fair Scheduling in the Distributed Query Processing Framework

Department of Computer EngineeringUtilizing caching facilities in modern query processing systems is getting more important as the capacity of main memory is having been greatly increasing. Especially in the data intensive applications, caching effect gives significant performance gain avoiding disk I/O which is highly expensive than memory access. Therefore data must be carefully distributed across back-end application servers to get advantages from caching as much as possible. On the other hand, load balance across back-end application servers is another concern the scheduler must consider. Serious load imbalance may result in poor performance even if the cache hit ratio is high. And the fact that scheduling decision which raises cache hit ratio sometimes results in load imbalance even makes it harder to make scheduling decision. Therefore we should find a scheduling algorithm which balances trade-off between load balance and cache hit ratio successfully. To consider both cache hit and load balance, we propose two semantic caching mechanisms DEMB and EM-KDE which successfully balance the load while keeping high cache hit ratio by analyzing and predicting trend of query arrival patterns. Another concern discussed in this paper is the environment with multiple front-end schedulers. Each scheduler can have different query arrival pattern from users. To reflect those differences of query arrival pattern from each front-end scheduler, we compare 3 algorithms which aggregate the query arrival pattern information from each front-end scheduler and evaluate them. To increase cache hit ratio in semantic caching scheduling further, migrating contents of cache to nearby server is proposed. We can increase cache hit count if data can be dynamically migrated to the server where the subsequent data requests supposed to be forwarded. Several migrating policies and their pros and cons will be discussed later. Finally, we introduce a MapReduce framework called Eclipse which takes full advantages from semantic caching scheduling algorithm mentioned above. We show that Eclipse outperforms other MapReduce frameworks in most evaluations.ope

DEMB: Cache-aware scheduling for distributed query processing

Leveraging data in distributed caches for large scale query processing applications is becoming more important, given current trends toward building large scalable distributed systems by connecting multiple heterogeneous less powerful machines rather than purchasing expensive homogeneous and very powerful machines. As more servers are added to such clusters, more memory is available for caching data objects across the distributed machines. However the cached objects are dispersed and traditional query scheduling policies that take into account only load balancing do not effectively utilize the increased cache space. We propose a new multi-dimensional range query scheduling policy for distributed query processing frameworks, called DEMB, that employs a probability distribution estimation derived from recent queries. DEMB accounts for both load balancing and the availability of distributed cached objects to both improve the cache hit rate for queries and thereby decrease query turnaround time and throughput. We experimentally demonstrate that DEMB produces better query plans and lower query response times than other query scheduling policies

Collaborative Multi-dimensional Dataset Processing with Distributed Cache Infrastructure in the Cloud

As modern large scale systems are built with a large number of independent small servers, it is becoming more important to orchestrate and leverage a large number of distributed buffer cache memory seamlessly. Several previous studies showed that with large scale distributed caching facilities, traditional resource scheduling policies often fail to exhibit high cache hit ratio and to achieve good system load balance. A scheduling policy that solely considers system load results in low cache hit ratio, and a scheduling policy that puts more emphasis on cache hit ratio than load balance suffers from system load imbalance. To maximize the overall system throughput, distributed caching facilities should balance the workloads and also leverage cached data at the same time. In this work, we present a distributed job processing framework that yields high cache hit ratio while achieving good system load balance, the two of which are most critical performance factors to improve overall system throughput and job response time. Our framework is a component-based distributed data analysis framework that supports geographically distributed multiple job schedulers. The job scheduler in our framework employs a distributed job scheduling policy -- DEMA that considers both cache hit ratio and system load. In this paper, we show collaborative task scheduling can even further improve the performance by increasing the overall cache hit ratio while achieving load balance. Our experiments show that the proposed job scheduling policies outperform legacy load-based job scheduling policy in terms of job response time, load balancing, and cache hit ratio

EM-KDE: A locality-aware job scheduling policy with distributed semantic caches

In modern query processing systems, the caching facilities are distributed and scale with the number of servers. To maximize the overall system throughput, the distributed system should balance the query loads among servers and also leverage cached results. In particular, leveraging distributed cached data is becoming more important as many systems are being built by connecting many small heterogeneous machines rather than relying on a few high-performance workstations. Although many query scheduling policies exist such as round-robin and load-monitoring, they are not sophisticated enough to both balance the load and leverage cached results. In this paper, we propose distributed query scheduling policies that take into account the dynamic contents of distributed caching infrastructure and employ statistical prediction methods into query scheduling policy. We employ the kernel density estimation derived from recent queries and the well-known exponential moving average (EMA) in order to predict the query distribution in a multi-dimensional problem space that dynamically changes. Based on the estimated query distribution, the front-end scheduler assigns incoming queries so that query workloads are balanced and cached results are reused. Our experiments show that the proposed query scheduling policy outperforms existing policies in terms of both load balancing and cache hit ratio. (C) 2015 Elsevier Inc. All rights reservedclose0

EclipseMR: Distributed and Parallel Task Processing with Consistent Hashing

We present EclipseMR, a novel MapReduce framework prototype that efficiently utilizes a large distributed memory in cluster environments. EclipseMR consists of double-layered consistent hash rings - a decentralized DHT-based file system and an in-memory key-value store that employs consistent hashing. The in-memory key-value store in EclipseMR is designed not only to cache local data but also remote data as well so that globally popular data can be distributed across cluster servers and found by consistent hashing. In order to leverage large distributed memories and increase the cache hit ratio, we propose a locality-aware fair (LAF) job scheduler that works as the load balancer for the distributed in-memory caches. Based on hash keys, the LAF job scheduler predicts which servers have reusable data, and assigns tasks to the servers so that they can be reused. The LAF job scheduler makes its best efforts to strike a balance between data locality and load balance, which often conflict with each other. We evaluate EclipseMR by quantifying the performance effect of each component using several representative MapReduce applications and show EclipseMR is faster than Hadoop and Spark by a large margin for various applications

Multi-dimensional multiple query scheduling with distributed semantic caching framework

It is becoming more important to leverage a large number of distributed cache memory seamlessly in modern large scale systems. Several previous studies showed that traditional scheduling policies often fail to exhibit high cache hit ratio and to achieve good system load balance with large scale distributed caching facilities. To maximize the system throughput, distributed caching facilities should balance the workloads and leverage cached data at the same time. In this work, we present a distributed job processing framework that yields high cache hit ratio while achieving balanced system load. Our framework employs a scheduling policy-DEMA that considers both cache hit ratio and system load and it supports geographically distributed multiple job schedulers. We show collaborative task scheduling and the data migration can even further improve the performance by increasing the cache hit ratio while achieving good load balance. Our experiments show that the proposed job scheduling policies outperform legacy load-based job scheduling policy in terms of job response time, load balancing, and cache hit ratioclose0