Cluster caching has been increasingly deployed in front of cloud storage to improve I/O performance. In shared, multi-tenant environments such as cloud datacenters, cluster caches are constantly contended by many users. Enforcing performance isolation between users hence becomes imperative to cluster caching. A user's caching performance critically depends on two factors: (1) the amount of cache allocation and (2) the load of servers in which its files are cached. However, existing cache sharing policies only provide guarantees on the amount of cache allocation, while remaining agnostic to the load of cache servers. Consequently, "mice" users having files co-located with "elephants" contributing heavy data accesses may experience extremely long latency, hence receiving no isolation. In this paper, we propose a Load-Aware Cache Sharing scheme (LACS) to enforce isolation between users. LACS keeps track of the load contributed by each user and reins back the congestions caused by elephant users by throttling their cache usage and network bandwidth. We have implemented LACS atop Alluxio, a popular cluster caching system. EC2 deployment shows that LACS achieves performance isolation in the presence of elephants, while improving the mean read latency by up to 80.4% (25.3% on average) over the state-of-the-art load balancing technique.