to cooling and power restrictions enforced by hardware. Access to data in any of the spun-up disks can be done with latency and bandwidth comparable to that of the traditional capacity tier. For instance, Pelican, OpenVault Knox, and ArticBlue provide between 1–2GB/s of throughput for reading data from spun-up disks. 2, 21, 27 However, accessing data on a spun-down disk takes several seconds, as the disk has to be spun up before data can be retrieved. Thus, CSDs form a perfect middle ground between HDD and tape with respect to both cost/GB and access latency.

On the application front, there is a clear bifurcation in demand between latency-sensitive interactive applications and latency insensitive batch applications. As interactive applications are isolated to the performance tier, the cold storage tier only has to cater to the bandwidth demands of latency-insensitive batch analytics applications. Nearline storage devices like tape libraries and CSD are capable of providing high-throughput access for sequentially accessed data. Thus, researchers have recently started investigating extensions to batch processing frameworks for enabling analytics directly over data stored in tape archives and CSD. For instance, Nakshatra implements prefetching and I/O scheduling extensions to Hadoop so that ma-preduce jobs can be scheduled to run directly on tape archives. 14 Skipper is a query-processing framework that uses adaptive query processing techniques in combination with customized caching and I/O scheduling to enable que-amortize the cost of random accesses.

However, the primary use of the capacity tier today is not sup-porting applications that require high-performance random accesses. Rather, it is to reduce the cost/GB of storing data over which latency-insensitive batch analytics can be performed. Indeed, Gray and Graefe noted that metrics like KB-accesses-per-second (Kaps) are less relevant for HDD and tape as they grow into infinite-capacity resources. 8 Instead, MB-accesses-per-second (Maps) and time to scan the whole devices are more pertinent to these high-density storage devices. Table 6 shows these new metrics and their values for DRAM, HDD, and tape. In addition to Kaps, Maps, and scan time, the table also shows $/Kaps, $/Maps, and $/TBscan, where costs are amortized over a three-year time frame as proposed by Gray and Graefe. 8

Looking at $/Kaps, we see that DRAM is five orders of magnitude cheaper than HDD, which, in turn, is six orders of magnitude cheaper than tape. This is expected given the huge disparity in random access latencies and is in accordance with the five-minute rule that favors using DRAM for randomly accessed data. Looking at $/Maps, we see that the difference between DRAM and HDD shrinks to roughly 1,000×. This is due to the fact that HDDs can provide much higher throughput for sequential data accesses over random ones. However, HDD continue to be six orders of magnitude cheaper than tape even for MB-sized random data accesses. This, also, is in accordance with the HDD/tape asymptote shown in Figure 2. Finally, $/TBscan paints a very different picture. While DRAM remains 300× cheaper than HDD, the difference between HDD and tape shrinks to 10×.

Comparing the $/TBscan values
with those reported in 1997, we can see
two interesting trends. First, the dispar-
ity between DRAM and HDD is growing
over time. In 1997, it was 13× cheaper
to use DRAM for a TBscan than HDD.
Today, it is 300× cheaper. This implies
that even for scan-intensive applica-
tions, unsurprisingly, optimizing for
performance requires avoiding using
HDD as the storage medium. Second,
the difference between HDD and tape
is following the opposite trend and
shrinking over time. In 1997, HDD was
70× cheaper than tape. However, today
it is only 10× cheaper. Unlike HDD, se-
quential data transfer bandwidth of
tape is predicted to double for the fore-
seeable future. Hence, this difference
is likely to shrink further. Thus, in the
near future, it might not make much of
a difference whether data is stored in a
tape or HDD with respect to the price
paid per TB scan.
Implications. Today, all data gener-
ated by an enterprise has to be stored
twice, once in the traditional HDD-
based capacity tier for enabling batch
analytics, and a second time in the
tape-based archival tier for meeting
regulatory compliance requirements.
The shrinking difference in $/TBscan
between HDD and tape suggests that
it might be economically beneficial to
merge the capacity and archival tiers
into a single cold storage tier. 3 However,
with such a merger, the cold storage tier
would no longer be a near-line tier that
is used rarely during disaster recovery,
but an online tier that is used for run-
ning batch analytics applications. Re-
cent hardware and application trends
indicate that it might be feasible to
build such a cold storage tier.
On the hardware front, storage ven-
dors have recently started building
new cold storage devices (CSD) for stor-
ing cold data. Each CSD is an ensemble
of HDDs grouped in a massive array of
idle disks (MAID) setup where only a
small subset of disks are active at any
given time. 2, 4, 27 For instance, Pelican
CSD pro vides 5PB of storage using
1, 152 SMR disks packed as a 52U rack
appliance. 2 However, only 8% of disks
can be spun up simultaneously due

Figure 2. Break-even interval asymptotes for DRAM–HDD and DRAM–tape cases.

DRAM-HDD DRAM-Tape

Br e ak -ev e ni nt er v al (m i n)

Page size (KB)

1

1E+09

100,000,000

10,000,000

1,000,000

10,000

1,000

100

10

1

1,000 1,000,000 1E+09 1E+ 12